Richard Gomulkiewicz

 

As I understand it I have the honor, of which I am most definetly not worthy, of being the first Gomulkiewicz Keynote Speaker. Many of you here knew him but for those prospective students who didn’t that good fortune to meet him, he was a PI here at WSU but sadly passed away in 2023. He was a theoretical population geneticist and had a reputation for being tough. More than one of my fellow grad students warned me away from chosing him to be on my commitee during me first year. I am so grateful that I chose not to listen to that advice.

Mathematical Geneology

 

Outline

  1. About me
  2. Adaptation in stochastic environments
  3. Some of my ML projects
    • Regression-based Machine Learning (WSU)
    • Classification-based Machine Learning (UIdaho)
    • Generative AI-based Machine Learning (EHA)
  4. Closing thoughts
  5. Questions

About Me

Adaptation in Stochastic Environments

  • Bet-Hedging
    • Definition: Increasing variance in fitness at the expense of lower average fitness to ensure survival across unpredictable conditions.
    • Examples: Plants producing seeds with staggered germination times.
  • Life History Adjustments
    • r-selection: High fecundity with low parental investment (e.g., many offspring).
    • K-selection: Fewer offspring with higher investment (e.g., longevity, parental care).
    • Dormancy: Delayed development (e.g., seed banks).
  • Boosting Genetic Diversity
    • Role: Buffers populations against fluctuations, ensuring some individuals thrive in any condition.
    • Mechanisms: Variation of mutation rate, sexual reproduction, dispersal / gene flow.
  • Phenotypic Plasticity
    • Definition: Non-parallel reaction norms among individuals with different genotypes in response to different environmental conditions. GxE interaction.
    • Examples: Seasonal coat color changes in animals; metabolic flexibility in plants.

These Hypotheses can Be Tested

“The fitness of a lineage in a fluctuating environment is the time average of its fitness over the sequence of static conditions it encounters.”1

Different Kinds of Mean

 

Speaking of statistics

A better definition

 

Large Language Models

Modeling Language

 

https://medium.com/data-science-at-microsoft/how-large-language-models-work-91c362f5b78f

Outline

  1. About me
  2. Adaptation in stochastic environments
  3. Some of my ML projects
    • Regression-based Machine Learning (WSU)
    • Classification-based Machine Learning (UIdaho)
    • Generative AI-based Machine Learning (EHA)
  4. Closing thoughts
  5. Questions

Regression-based Machine Learning (WSU)

Can we reconstruct historical of range expansion routes?

 

Time Distance Of Arrival (TDOA)

 

Westward range expansion from middle latitudes explains the Mississippi River discontinuity in a forest herb of eastern North America2

Identifying the refugia

Sweeps in space: leveraging geographic data to identify beneficial alleles in Anopheles gambiae3

Classification-based Machine Learning (UIdaho)

Land Cover Classification

Predicting the fine-scale spatial distribution of zoonotic reservoirs using computer vision4

Zoonotic Reservoirs

Predicting the fine-scale spatial distribution of zoonotic reservoirs using computer vision4

Fine-scale Predictors

Predicting the fine-scale spatial distribution of zoonotic reservoirs using computer vision4

Generative AI-based Machine Learning (EHA)

EcoHealth Alliance is a nonprofit dedicated to using science-based solutions to prevent pandemics and promote conservation

World Organisation for Animal Health (WOAH)

Quantifying Underreporting in Wildlife Disease Surveillance

Automating a Lit Search

Quantifying Underreporting in Wildlife Disease Surveillance

Correcting for LLM errors using simulation extrapolation (SIMEX)

Outline

  1. About me
  2. Adaptation in stochastic environments
  3. Some of my ML projects
    • Regression-based Machine Learning (WSU)
    • Classification-based Machine Learning (UIdaho)
    • Generative AI-based Machine Learning (EHA)
  4. Closing thoughts
  5. Questions

Do I still think science is a viable career path in 2025?

Yes

But sometimes it feels like

For example,

Almost all of my academic friends, have all worked in both academic and non-academic environments at some point

I know post-graduates who are

  • Program managers
  • Database managements
  • Data librarians
  • Data scientists
  • Mathematical Statistician
  • Researcher at the Smithsonians
  • IT / AWS specialists
  • Public health educators
  • Consultants
  • Environmental health professionals
  • Biotechnology startup researchers

Adaptation in Stochastic Research Environments

  • Bet-Hedging
    • Definition: Increasing variance in fitness at the expense of lower average fitness to ensure survival across unpredictable conditions.
    • Examples: Plants producing seeds with staggered germination times.
  • Life History Adjustments
    • r-selection: High fecundity with low parental investment (e.g., many offspring).
    • K-selection: Fewer offspring with higher investment (e.g., longevity, parental care).
    • Dormancy: Delayed development (e.g., seed banks).
  • Boosting Genetic Diversity
    • Role: Buffers populations against fluctuations, ensuring some individuals thrive in any condition.
    • Mechanisms: Variation of mutation rate, sexual reproduction, dispersal / gene flow.
  • Phenotypic Plasticity
    • Definition: Non-parallel reaction norms among individuals with different genotypes in response to different environmental conditions. GxE interaction.
    • Examples: Seasonal coat color changes in animals; metabolic flexibility in plants.

But what about being replaced by AI?

But what about being replaced by AI?

But what about being replaced by AI?

More importantly, an generative AI is a based on massively complicated statistical engines designed to answer the question, “what should I say next?”

But what about being replaced by AI?

 

    If you make a complicated model of nature,

But what about being replaced by AI?

 

    If you make a complicated model of nature,
    now you have two things you don’t understand.
          -Alan Hastings

But what about being replaced by AI?

But what about being replaced by AI?

 

    AI is a tool to help people answer questions, but we are the people who ask questions.

Advice from my friends

Focus on transferrable skills like communication, technical writing, data viz, coding, problem solving.

Advice from my friends

  1. Take a grant writing class
  2. Take a coding class
  3. Learn GIS, particularly if doing fieldwork
  4. Take a ‘how to find jobs seminar’

Advice from my friends

•⁠ ⁠The best thing about SBS (and probably grad school anywhere) is the people. Make a point of meeting them, talking to them, asking for their help, and offering yours when you can. That will also have the side-effect of making you one of those great people folks meet in grad school.

•⁠ ⁠⁠From the day you come in to the day you leave, remember that you’re here to learn. Try stuff, fail, ask for help, change directions. Being bad at something is good. That’s just something you can try to improve at, which is why you’re here.

Advice from my friends

  1. Self-Evaluated Workload: Prioritize balance and adjust work hours based on personal assessment, not peer pressure or toxic “overwork” culture.

  2. Critical Analysis: Always question why specific methods or techniques are used in research; avoid adopting approaches solely because they’re precedent—understand their rationale.

  3. Workflow Investment: Develop and maintain efficient workflows tailored to your needs, even if it requires upfront time.

  4. Advisor Relationship: Choose an advisor you can collaborate with effectively, as their mentorship profoundly impacts your PhD experience. Proactively nurture this relationship to navigate challenges.

My advice

Doing science beyond grad school involves

  1. Learn project management!
  2. Leveraging your network
  3. Looking to the future

Useful tools

References

1.
Abreu, C. I., Mathur, S. & Petrov, D. A. Environmental memory alters the fitness effects of adaptive mutations in fluctuating environments. Nat. Ecol. Evol. 8, 1760–1775 (2024).
2.
Prior, C. J., Layman, N. C., Koski, M. H., Galloway, L. F. & Busch, J. W. Westward range expansion from middle latitudes explains the mississippi river discontinuity in a forest herb of eastern north america. Molecular Ecology 29, 4473–4486 (2020).
3.
Rehmann, C. T., Small, S. T., Ralph, P. L. & Kern, A. D. Sweeps in space: Leveraging geographic data to identify beneficial alleles in anopheles gambiae. bioRxiv (2025) doi:10.1101/2025.02.07.637123.
4.
Layman, N. C. et al. Predicting the fine‐scale spatial distribution of zoonotic reservoirs using computer vision. Ecology Letters 26, 1974–1986 (2023).